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Jayanthi M,
Subashini G,
Aravind S,
Gobi S,
- Assistant Professor, Dept of CSE, KalaignarKarunanidhi Institute of Technology, Coimbatore, India
- Student, Dept of CSE, KalaignarKarunanidhi Institute of Technology, Coimbatore, India
- Student, Dept of CSE, KalaignarKarunanidhi Institute of Technology, Coimbatore, India
- Student, Dept of CSE, KalaignarKarunanidhi Institute of Technology, Coimbatore, India
Abstract
Many avoidable deaths globally are caused by CVD, often due to individuals remaining unaware of their risk factors until severe symptoms, such as heart attacks or strokes, appear. This study utilizes retinal images as the dataset to explore the potential of retinal imaging as a non-invasive diagnostic tool for early detection of cardiovascular diseases (CVD). The delay in diagnosis and treatment highlights the need for sophisticated diagnostic instruments that can identify cardiovascular risk factors early. Retinal imaging has gained increasing attention as it provides valuable insights into an individual’s overall health, particularly the cardiovascular system, due to the direct connection between the retina and the central nervous system. Since retinal blood vessels can be imaged and analyzed, abnormalities in these vessels may indicate the presence of CVD, making retinal imaging a promising non-invasive approach. With an accuracy of 84%, this method has demonstrated effectiveness in detecting early signs of conditions like hypertension, diabetes, and atherosclerosis, which are closely linked to cardiovascular risk. Retinal changes, such as vessel narrowing, microaneurysms, and hemorrhages, can act as biomarkers for systemic diseases, helping clinicians assess a patient’s cardiovascular health more efficiently. Recent advancements in image processing, deep learning, and artificial intelligence further enhance the precision of detecting these abnormalities, with AI algorithms identifying patterns that might be overlooked by the human eye. Automated analysis of retinal images enables large-scale screenings, making early detection more accessible and cost-effective. This approach demonstrates promising potential in enabling early detection and proactive interventions, potentially reducing the global burden of CVD, lowering healthcare costs, and improving long-term patient outcomes through timely lifestyle modifications and medical interventions.
Keywords: Vascular Disease Detection, Inception V3, PYTORCH, Google Colab, Image analysis, Health monitoring
[This article belongs to Research and Reviews : A Journal of Medical Science and Technology ]
Jayanthi M, Subashini G, Aravind S, Gobi S. An Expected Cardiovascular Disease Detection Using Deep Learning Techniques. Research and Reviews : A Journal of Medical Science and Technology. 2025; 14(02):-.
Jayanthi M, Subashini G, Aravind S, Gobi S. An Expected Cardiovascular Disease Detection Using Deep Learning Techniques. Research and Reviews : A Journal of Medical Science and Technology. 2025; 14(02):-. Available from: https://journals.stmjournals.com/rrjomst/article=2025/view=225137
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| Volume | 14 |
| Issue | 02 |
| Received | 26/03/2025 |
| Accepted | 09/07/2025 |
| Published | 29/08/2025 |
| Publication Time | 156 Days |
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